The Single Seizure Clinic Model: Does It Work? Effects on Wait-times and Patient Outcomes (S6.005)
Bibliographic record
Abstract
OBJECTIVE: To compare the effectiveness of a specialized urgent-access single seizure clinic (SSC) versus standard care. BACKGROUND: Care for patients with seizures is fragmented. It is unknown whether specialist clinics improve outcomes for patients requiring workup for seizure(s). There is no high-quality evidence to describe SSC effectiveness. DESIGN/METHODS: A prospective study of 200 patients referred to our SSC for first seizure evaluation. Demographic, clinical, and paraclinicial variables were analyzed against historical controls. Binary logistic regression analysis was conducted to predict the impact of dichotomized variables on predicting epilepsy. RESULTS: Mean patient age was 42.1 years (range 14-88). Referral sources were predominantly emergency department and family physicians. A diagnosis was established at first contact in 80.9[percnt] of cases. 16.1[percnt] of patients required a second a visit. 0.5[percnt] of patients required three consultations. 82/200 (41[percnt]) patients were diagnosed with epilepsy. Syncope was found in 24.5[percnt], single unprovoked seizure in 14.4[percnt], and alcohol withdrawal seizures in 4.6[percnt]. Mean wait-time for first assessment was reduced by 71[percnt] (23.6 SSC versus 80.1 days standard care). Mean wait-time for an EEG was 4.0 days (37.1 days standard care). In 134/200 cases a CT scan had been performed. The wait-time for an MRI requested by the SSC was 44.9 days (81.3 days standard care). 63 patients were started on anti-epileptic drugs, with 63.50[percnt] starting lamotrigine, 7[percnt] levetiracetam, 5[percnt] phenytoin, and 5[percnt] topiramate. Presence of generalized spike-wave discharges (odds-ratio (OR)=12.8;CI:5.3-30.7;p<0.00) or focal spike-wave (OR=6.8;CI:1.9-23.6;p=0.003), tongue trauma (OR=6.3;CI:2.9-13.9;p<0.001), and pre-assessment stratification as high risk for seizure recurrence (OR=4.3;CI:1.7-10.9;p=0.002) strongly predicted epilepsy. SSC physicians were 17.1 more likely to accurately diagnose epilepsy versus nurses; there was a non-significant correlation between physician and nurse diagnoses. CONCLUSIONS: The SSC reflects an effective platform for single point-of-access care for seizure workup. This model reduces wait-times, improves patient access,and streamlines care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".